Hi Roberto, >> but I can't figure out the /(Lobe*Tissue) part...
This type of nesting is easier to do using lmer(). To do it using lme() you have to generate the crossed factor yourself. Do something like this: ## tfac <- with(vslt, interaction(Lobe, Tissue, drop=T)) str(tfac); head(tfac) mod2<-lme(Volume ~ Val*Lobe*Tissue, random = ~1|Subject/tfac, data = vslt) Pre-Scriptum: You can also use ?":" but ?interaction is more flexible and powerful. Regards, Mark. roberto toro wrote: > > Hello, > > I'm using aov() to analyse changes in brain volume between males and > females. For every subject (there are 331 in total) I have 8 volume > measurements (4 different brain lobes and 2 different tissues > (grey/white matter)). The data looks like this: > > Subject Sex Lobe Tissue Volume > subect1 1 F g 262374 > subect1 1 F w 173758 > subect1 1 O g 67155 > subect1 1 O w 30067 > subect1 1 P g 117981 > subect1 1 P w 85441 > subect1 1 T g 185241 > subect1 1 T w 83183 > subect2 1 F g 255309 > subect2 1 F w 164335 > subect2 1 O g 71769 > subect2 1 O w 31879 > subect2 1 P g 120518 > subect2 1 P w 90334 > subect2 1 T g 168413 > subect2 1 T w 75790 > subect3 0 F g 243621 > subect3 0 F w 167025 > subect3 0 O g 65998 > subect3 0 O w 29758 > subect3 0 P g 118026 > subect3 0 P w 91903 > subect3 0 T g 156279 > subect3 0 T w 82349 > .... > > I'm trying to see if there is an interaction Sex*Lobe*Tissue. This is > the command I use with aov(): > > mod1<-aov(Volume~Sex*Lobe*Tissue+Error(Subject/(Lobe*Tissue)),data.vslt) > > Subject is a random effect, Sex, Lobe and Tissue are fixed effects; > Sex is an outer factor (between subjects), and Lobe and Tissue are > inner factors (within-subjects); and there is indeed a significant > 3-way interaction. > > I was told, however, that the results reported by aov() may depend on > the order of the factors > (type I anova), and that is better to use lme() or lmer() with type > II, but I'm struggling to find the right syntaxis... > > To begin, how should I write the model using lme() or lmer()?? > > I tried this with lme(): > > gvslt<-groupedData(Volume~1|Subject,outer=~Val,inner=list(~Lobe,~Tissue),data=vslt) > mod2<-lme(Volume~Val*Lobe*Tissue,random=~1|Subject,data=gvslt) > > but I have interaction terms for every level of Lobe and Tissue, and 8 > times the number of DF I should have... (around 331*8 instead of > ~331). > > Using lmer(), the specification of Subject as a random effect is > straightforward: > > mod2<-lmer(Volume~Sex*Lobe*Tissue+(1|Subject),data.vslt) > > but I can't figure out the /(Lobe*Tissue) part... > > Thank you very much in advance! > roberto > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/Help-please%21-How-to-code-a-mixed-model-with-2-within-subject-factors-using-lme-or-lmer--tp19479860p19480387.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.